Inductive and Transductive Transfer Learning for Zero-day Attack Detection
Berella Sameera1, Andhavarapu Bhanusri2, M. Shashi3
1Nerella Sameera, Research Scholar, Dept. of CS&SE, Andhra University College of Engineering (A), Andhra University, Visakhapatnam, India.
2Andhavarapu Bhanusri, Mtech Scholar, Dept. of CS&SE, Andhra University College of Engineering (A), Andhra University, Visakhapatnam, India.
3M. Shashi, Professor, Dept. of CS&SE, Andhra University College of Engineering (A), Andhra University, Visakhapatnam, India.
Manuscript received on 26 August 2019. | Revised Manuscript received on 08 September 2019. | Manuscript published on 30 September 2019. | PP: 1765-1768 | Volume-8 Issue-11, September 2019. | Retrieval Number: K17580981119/2019©BEIESP | DOI: 10.35940/ijitee.K1758.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Upon application of supervised machine learning techniques Intrusion Detection Systems (IDSs) are successful in detecting known attacks as they use predefined attack signatures. However, detecting zero-day attacks is challenged because of the scarcity of the labeled instances for zero-day attacks. Advanced research on IDS applies the concept of Transfer Learning (TL) to compensate the scarcity of labeled instances of zero-day attacks by making use of abundant labeled instances present in related domain(s). This paper explores the potential of Inductive and Transductive transfer learning for detecting zero-day attacks experimentally, where inductive TL deals with the presence of minimal labeled instances in the target domain and transductive TL deals with the complete absence of labeled instances in the target domain. The concept of domain adaptation with manifold alignment (DAMA) is applied in inductive TL where the variant of DAMA is proposed to handle transductive TL due to non-availability of labeled instances. NSL_KDD dataset is used for experimentation.
Keywords: Inductive transfer learning, Manifold alignment, Transductive transfer learning, Transfer Learning, Zero-day attack.
Scope of the Article: